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Multivariate spatial models for event data.

A H Leyland1, I H Langford, J Rasbash

  • 1MRC Social and Public Health Sciences Unit, University of Glasgow, Scotland. a.leyland@msoc.mrc.gla.ac.uk

Statistics in Medicine
|August 29, 2000
PubMed
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Spatial modeling enhances small area estimates by incorporating geographical data and additional information. This study used multivariate spatial models to analyze mortality patterns for neoplasms and circulatory disease in Greater Glasgow.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Small area estimation often faces data sparsity challenges.
  • Geographical location and additional covariates can improve event rate estimations.
  • Simultaneous modeling of multiple outcomes can reveal complex relationships.

Purpose of the Study:

  • To enhance small area event rate estimates using spatial modeling.
  • To simultaneously predict mortality from neoplasms and circulatory disease.
  • To quantify spatial patterns and the impact of socio-economic deprivation on mortality.

Main Methods:

  • Utilized multi-level spatial models to predict multiple outcomes concurrently.
  • Employed a multivariate data structure within the spatial model.

Related Experiment Videos

  • Obtained estimates using iterative generalized least squares in MLwiN software.
  • Analyzed mortality data for 143 postcode sectors in Greater Glasgow, Scotland.

    • Main Results:

    • Identified localized 'pockets' of high mortality for neoplasms.
    • Observed a smoother spatial pattern for circulatory disease mortality.
    • Quantified correlations between causes and areas, and between areas and causes.
    • Determined the relative contributions of heterogeneous and spatial model components.

    Conclusions:

    • Spatial modeling effectively enhances small area mortality estimates.
    • Deprivation significantly influences mortality patterns, particularly for circulatory disease.
    • Neoplasm mortality shows more localized spatial variation compared to circulatory disease mortality.